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Journal of Nonparametric Statistics is a peer-reviewed journal published by Taylor & Francis, covering Statistical Methods and Inference, Advanced Statistical Methods and Models, Statistical Methods and Bayesian Inference. Over its lifetime it has published 1,591 papers, which have received 21,375 citations.
| Publisher | Taylor & Francis |
|---|---|
| Reference style | Author–year (Chicago, T&F) Author–year — (Smith, 2023) in the text Smith, Ada, Ben Jones, and Cara Lee. 2023. "A Representative Article Title." Journal of Nonparametric Statistics 12 (3): 45–58.
Formats any DOI in Journal of Nonparametric Statistics style. No sign-up. |
| Publishes research in | Statistical Methods and Inference Advanced Statistical Methods and Models Statistical Methods and Bayesian Inference Bayesian Methods and Mixture Models Statistical Distribution Estimation and Applications |
| ISSN | 1026-7654 |
| Citation impact (2-yr) | 0.89 |
| h-index | 61 |
| i10-index | 541 |
| Top institutions publishing here | Centre National de la Recherche Scientifique |
| Journal website | www.tandfonline.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
K-fold cross-validation (CV) is widely adopted as a model selection criterion. In K-fold CV, $ (K-1) $ (K−1) folds are used for model construction and the hold-out fold is allocated to model validation. This implies model construction is more emphasised than the model validation procedure. However, some studies have revealed that more emphasis on the…
Cross-validation (CV) type of methods have been widely used to facilitate model estimation and variable selection. In this work, we suggest a new K-fold CV procedure to select a candidate ‘optimal’ model from each hold-out fold and average the K candidate ‘optimal’ models to obtain the ultimate model. Due to the averaging effect, the variance…
We consider bandwidth matrix selection for bivariate kernel density estimators. The majority of work in this area has been directed towards selection of diagonal bandwidth matrices, but full bandwidth matrices can give markedly better performance for some types of target density. Our methodological contribution has been to develop a new version of the plug-in selector…
We propose a general class of asymptotically distribution-free tests of a linear hypothesis in the linear regression model. The tests are based on regression rank scores, recently introduced by Gutenbrunner and Jurecková (1992) as dual variables to the regression quantiles of Koenker and Bassett (1978). Their properties are analogous to those of the corresponding rank…
This paper presents a simple test for dependence in the residuals of a linear parametric time series model fitted to non gaussian data. The test statistic is a third order extension of the standard correlation test for whiteness. but the number of lags used in this test is a function of the sample size. The…
